Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
100K<n<1M
License:
| annotations_creators: | |
| - crowdsourced | |
| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 100K<n<1M | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - token-classification | |
| task_ids: | |
| - named-entity-recognition | |
| paperswithcode_id: broad-twitter-corpus | |
| pretty_name: Broad Twitter Corpus | |
| # Dataset Card for broad_twitter_corpus | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-instances) | |
| - [Data Splits](#data-instances) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| ## Dataset Description | |
| - **Homepage:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus) | |
| - **Repository:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus) | |
| - **Paper:** [http://www.aclweb.org/anthology/C16-1111](http://www.aclweb.org/anthology/C16-1111) | |
| - **Leaderboard:** [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter) | |
| - **Point of Contact:** [Leon Derczynski](https://github.com/leondz) | |
| ### Dataset Summary | |
| This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities. | |
| See the paper, [Broad Twitter Corpus: A Diverse Named Entity Recognition Resource](http://www.aclweb.org/anthology/C16-1111), for details. | |
| ### Supported Tasks and Leaderboards | |
| * Named Entity Recognition | |
| * On PWC: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter) | |
| ### Languages | |
| English from UK, US, Australia, Canada, Ireland, New Zealand; `bcp47:en` | |
| ## Dataset Structure | |
| ### Data Instances | |
| Feature |Count | |
| ---|---: | |
| Documents |9 551 | |
| Tokens |165 739 | |
| Person entities |5 271 | |
| Location entities |3 114 | |
| Organization entities |3 732 | |
| ### Data Fields | |
| Each tweet contains an ID, a list of tokens, and a list of NER tags | |
| - `id`: a `string` feature. | |
| - `tokens`: a `list` of `strings` | |
| - `ner_tags`: a `list` of class IDs (`int`s) representing the NER class: | |
| ``` | |
| 0: O | |
| 1: B-PER | |
| 2: I-PER | |
| 3: B-ORG | |
| 4: I-ORG | |
| 5: B-LOC | |
| 6: I-LOC | |
| ``` | |
| ### Data Splits | |
| Section|Region|Collection period|Description|Annotators|Tweet count | |
| ---|---|---|---|---|---: | |
| A | UK| 2012.01| General collection |Expert| 1000 | |
| B |UK |2012.01-02 |Non-directed tweets |Expert |2000 | |
| E |Global| 2014.07| Related to MH17 disaster| Crowd & expert |200 | |
| F |Stratified |2009-2014| Twitterati |Crowd & expert |2000 | |
| G |Stratified| 2011-2014| Mainstream news| Crowd & expert| 2351 | |
| H |Non-UK| 2014 |General collection |Crowd & expert |2000 | |
| The most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived. | |
| **Test**: Section F | |
| **Development**: Section H (the paper says "second half of Section H" but ordinality could be ambiguous, so it all goes in. Bonne chance) | |
| **Training**: everything else | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [Needs More Information] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [Needs More Information] | |
| #### Who are the source language producers? | |
| [Needs More Information] | |
| ### Annotations | |
| #### Annotation process | |
| [Needs More Information] | |
| #### Who are the annotators? | |
| [Needs More Information] | |
| ### Personal and Sensitive Information | |
| [Needs More Information] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [Needs More Information] | |
| ### Discussion of Biases | |
| [Needs More Information] | |
| ### Other Known Limitations | |
| [Needs More Information] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [Needs More Information] | |
| ### Licensing Information | |
| Creative Commons Attribution 4.0 International (CC BY 4.0) | |
| ### Citation Information | |
| ``` | |
| @inproceedings{derczynski2016broad, | |
| title={Broad twitter corpus: A diverse named entity recognition resource}, | |
| author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian}, | |
| booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers}, | |
| pages={1169--1179}, | |
| year={2016} | |
| } | |
| ``` | |
| ### Contributions | |
| Author-added dataset [@leondz](https://github.com/leondz) | |